新型合成大麻素ADB-BUTINACA在不同时间段斑马鱼体内的代谢组学分析

Metabolomic Analysis of a Novel Synthetic Cannabinoid ADB-BUTINACA in Zebrafish in Different Time Periods

采用液相色谱-高分辨质谱技术对不同时间段斑马鱼体内ADB-BUTINACA的21种代谢产物进行分析。首先采用正交信号变换的偏最小二乘判别分析和层次聚类分析方法筛选出7种具有显著性差异的组间代谢物,以7种差异代谢物为特征建立Stacking集成学习模型,对4组不同时间段的斑马鱼体内样本进行分类预测。结果显示,模型预测准确率高达98%,表明筛选的潜在差异代谢物能够有效反映不同时间段原药在斑马鱼体内的变化情况;对7种潜在差异代谢物在4类样本体内的含量变化进行富集分析,结果表明差异代谢物的总体含量随着染毒时间的增加而降低,各类代谢物的含量分布由最初的不均衡趋向于均衡分布。此外,实验发现大部分差异代谢物的代谢路径与羟基化反应密切相关,推测原药在生物体内发生羟基化反应与给药时间推断方面具有一定关联性。实验结果可为药物服用时间推断等相关领域分析提供依据。

Liquid chromatography-high resolution mass spectrometry was ultilized for the analysis on 21 metabolites of ADB-BUTINACA in zebrafish over different time periods in this paper.First⁃ly,the partial least squares discriminant analysis and hierarchical clustering analysis of orthogonal signal transformation were used to screen out 7 metabolites with significant differences between groups.Furthermore,a Stacking integrated learning model was established with the 7 differential metabolites as the characteristics for classification and prediction on 4 groups of samples in zebrafish in different time periods.It was found that the prediction accuracy of the model was as high as 98%,indicating that the potential differential metabolites screened could effectively reflect the variation of the original drug in zebrafish in different time periods.The content changes of metabolites in the 4 groups of samples were enriched and analyzed.The results showed that the overall content of differen⁃tial metabolites decreased with the increase of exposure time,and the content distribution of various metabolites tended to be balanced from the initial imbalance.In addition,the experiment showed that most of the differential metabolite metabolic pathways closely related to hydroxylation reactions,and it was speculated that the hydroxylation reactions of the original drug in vivo had a certain correlation with the estimation of administration time.The experimental results could provide a basis for analysis in related fields such as drug taking time estimation.